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Analysis

1 / 2,458
1.20200342307Swarm fair deep reinforcement learning
US - 29.10.2020
Int.Class G06N 3/08
GPHYSICS
06COMPUTING; CALCULATING OR COUNTING
NCOMPUTING ARRANGEMENTS BASED ON SPECIFIC COMPUTATIONAL MODELS
3Computing arrangements based on biological models
02Neural networks
08Learning methods
 Appl.No 16395187 Applicant International Business Machines Corporation Inventor Aaron K. Baughman

Fair deep reinforcement learning is provided. A microstate of an environment and reaction of items in a plurality of microstates within the environment are observed after an agent performs an action in the environment. Semi-supervised training is utilized to determine bias weights corresponding to the action for the microstate of the environment and the reaction of the items in the plurality of microstates within the environment. The bias weights from the semi-supervised training are merged with non-bias weights using an artificial neural network. Over time, it is determined where bias is occurring in the semi-supervised training based on merging the bias weights with the non-bias weights in the artificial neural network. A deep reinforcement learning model that decreases reliance on the bias weights is generated based on determined bias to increase fairness.

2.12274503Myopia ocular predictive technology and integrated characterization system
US - 15.04.2025
Int.Class A61B 3/14
AHUMAN NECESSITIES
61MEDICAL OR VETERINARY SCIENCE; HYGIENE
BDIAGNOSIS; SURGERY; IDENTIFICATION
3Apparatus for testing the eyes; Instruments for examining the eyes
10Objective types, i.e. instruments for examining the eyes independent of the patients perceptions or reactions
14Arrangements specially adapted for eye photography
 Appl.No 18778027 Applicant COGNITIVECARE INC. Inventor Venkata Narasimham Peri

According to an embodiment, disclosed is a system comprising a processor wherein the processor is configured to receive an input data comprising an image of an ocular region of a user, clinical data of the user, and external factors; extract, using an image processing module comprising adaptive filtering techniques, ocular characteristics, combine, using a multimodal fusion module, the input data to determine a holistic health embedding; detect, based on a machine learning model and the holistic health embedding, a first output comprising likelihood of myopia, and severity of myopia; predict, based on the machine learning model and the holistic health embedding, a second output comprising an onset of myopia and a progression of myopia in the user; and wherein the machine learning model is a pre-trained model; and wherein the system is configured for myopia prognosis powered by multimodal data.

3.20140188462System and method for analyzing ambiguities in language for natural language processing
US - 03.07.2014
Int.Class G06F 17/00
GPHYSICS
06COMPUTING; CALCULATING OR COUNTING
FELECTRIC DIGITAL DATA PROCESSING
17Digital computing or data processing equipment or methods, specially adapted for specific functions
 Appl.No 14201974 Applicant Zadeh Lotfi A. Inventor Zadeh Lotfi A.

Specification covers new algorithms, methods, and systems for artificial intelligence, soft computing, and deep learning/recognition, e.g., image recognition (e.g., for action, gesture, emotion, expression, biometrics, fingerprint, facial, OCR (text), background, relationship, position, pattern, and object), large number of images (“Big Data”) analytics, machine learning, training schemes, crowd-sourcing (using experts or humans), feature space, clustering, classification, similarity measures, optimization, search engine, ranking, question-answering system, soft (fuzzy or unsharp) boundaries/impreciseness/ambiguities/fuzziness in language, Natural Language Processing (NLP), Computing-with-Words (CWW), parsing, machine translation, sound and speech recognition, video search and analysis (e.g. tracking), image annotation, geometrical abstraction, image correction, semantic web, context analysis, data reliability (e.g., using Z-number (e.g., “About 45 minutes; Very sure”)), rules engine, control system, autonomous vehicle, self-diagnosis and self-repair robots, system diagnosis, medical diagnosis, biomedicine, data mining, event prediction, financial forecasting, economics, risk assessment, e-mail management, database management, indexing and join operation, memory management, and data compression.

4.20220012315AUTHORIZATION SYSTEM BASED ON BIOMETRIC IDENTIFICATION AND METHOD THEREFOR
US - 13.01.2022
Int.Class G06F 21/32
GPHYSICS
06COMPUTING; CALCULATING OR COUNTING
FELECTRIC DIGITAL DATA PROCESSING
21Security arrangements for protecting computers, components thereof, programs or data against unauthorised activity
30Authentication, i.e. establishing the identity or authorisation of security principals
31User authentication
32using biometric data, e.g. fingerprints, iris scans or voiceprints
 Appl.No 16922005 Applicant NATIONAL TAIWAN UNIVERSITY Inventor An-Yeu WU

An authorization system based on biometric identification and a method thereof are provided. An incomplete physiological signal of a subject is obtained. Next, the incomplete physiological signal is analyzed according to a machine learning model to identify an identity corresponding to the subject, and then output identity information. Then, whether the authorization is obtained based on the identity information is determined. When the authorization is obtained, the authorization content is provided. Therefore, in the case where the physiological signal is an incomplete signal, it is possible to perform identity recognition based on the machine learning model, and then to determine whether to provide the corresponding authorization content, so as to achieve the technical efficacy of recognition stability.

5.WO/2022/155555SYSTEMS AND METHODS FOR DERIVING HEALTH INDICATORS FROM USER-GENERATED CONTENT
WO - 21.07.2022
Int.Class G16H 50/20
GPHYSICS
16INFORMATION AND COMMUNICATION TECHNOLOGY SPECIALLY ADAPTED FOR SPECIFIC APPLICATION FIELDS
HHEALTHCARE INFORMATICS, i.e. INFORMATION AND COMMUNICATION TECHNOLOGY SPECIALLY ADAPTED FOR THE HANDLING OR PROCESSING OF MEDICAL OR HEALTHCARE DATA
50ICT specially adapted for medical diagnosis, medical simulation or medical data mining; ICT specially adapted for detecting, monitoring or modelling epidemics or pandemics
20for computer-aided diagnosis, e.g. based on medical expert systems
 Appl.No PCT/US2022/012645 Applicant MY LUA LLC Inventor CONWARD, Michael
The present disclosure relates to systems and methods for generating priority lists and/or predictions or identifications of root causes of acute or chronic conditions. In one exemplary embodiment, a method comprises aggregating data corresponding to a plurality of individuals, the data comprising, for each individual, user-generated content and/or biometric data; generating, from a machine learning model that utilizes the aggregated user-generated content and/or biometric data as input, one or more of a priority list for the plurality of individuals, or, for each individual, a prediction, diagnosis, or identification of one or more root causes of one or more acute or chronic conditions of the individual.
6.20140201126Method and system for feature detection
US - 17.07.2014
Int.Class G06N 7/00
GPHYSICS
06COMPUTING; CALCULATING OR COUNTING
NCOMPUTING ARRANGEMENTS BASED ON SPECIFIC COMPUTATIONAL MODELS
7Computing arrangements based on specific mathematical models
 Appl.No 14218923 Applicant Lotfi A. Zadeh Inventor Lotfi A. Zadeh

Specification covers new algorithms, methods, and systems for artificial intelligence, soft computing, and deep learning/recognition, e.g., image recognition (e.g., for action, gesture, emotion, expression, biometrics, fingerprint, facial, OCR (text), background, relationship, position, pattern, and object), Big Data analytics, machine learning, training schemes, crowd-sourcing (experts), feature space, clustering, classification, SVM, similarity measures, modified Boltzmann Machines, optimization, search engine, ranking, question-answering system, soft (fuzzy or unsharp) boundaries/impreciseness/ambiguities/fuzziness in language, Natural Language Processing (NLP), Computing-with-Words (CWW), parsing, machine translation, sound and speech recognition, video search and analysis (e.g. tracking), image annotation, geometrical abstraction, image correction, semantic web, context analysis, data reliability, Z-number, Z-Web, Z-factor, rules engine, control system, autonomous vehicle, self-diagnosis and self-repair robots, system diagnosis, medical diagnosis, biomedicine, data mining, event prediction, financial forecasting, economics, risk assessment, e-mail management, database management, indexing and join operation, memory management, data compression, event-centric social network, Image Ad Network.

7.20230199025ACCOUNT CLASSIFICATION USING A TRAINED MODEL AND SIGN-IN DATA
US - 22.06.2023
Int.Class H04L 9/40
HELECTRICITY
04ELECTRIC COMMUNICATION TECHNIQUE
LTRANSMISSION OF DIGITAL INFORMATION, e.g. TELEGRAPHIC COMMUNICATION
9Arrangements for secret or secure communications; Network security protocols
40Network security protocols
 Appl.No 17557254 Applicant Microsoft Technology Licensing, LLC Inventor Ye XU

A trained machine learning model distinguishes between human-driven accounts and machine-driven accounts by performing anomaly detection based on sign-in data and optionally also based on directory data. This machine versus human distinction supports security improvements that apply security controls and other risk management tools and techniques which are specifically tailored to the kind of account being secured. Formulation heuristics can improve account classification accuracy by supplementing a machine learning model anomaly detection result, e.g., based on directory information, kind of IP address, kind of authentication, or various sign-in source characteristics. Machine-driven accounts masquerading as human-driven may be identified as machine-driven. Reviewed classifications may serve as feedback to improve the model's accuracy. A precursor machine learning model may generate training data for training a production account classification machine learning model.

8.WO/2019/028279METHODS AND SYSTEMS FOR OPTIMIZING ENGINE SELECTION USING MACHINE LEARNING MODELING
WO - 07.02.2019
Int.Class G06N 99/00
GPHYSICS
06COMPUTING; CALCULATING OR COUNTING
NCOMPUTING ARRANGEMENTS BASED ON SPECIFIC COMPUTATIONAL MODELS
99Subject matter not provided for in other groups of this subclass
 Appl.No PCT/US2018/045051 Applicant VERITONE, INC. Inventor STEELBERG, Chad
A system for optimizing selection of transcription engines using a combination of selected machine learning models. The system includes a plurality of preprocessors that generate a plurality of features from a media data set. The system further includes a deep learning neural network model, a gradient boosted machine model and a random forest model used in generating a ranked list of transcription engines. A transcription engine is selected from the ranked list of transcription engines to generate a transcript for the media dataset.
9.20200064444METHOD, APPARATUS, AND SYSTEM FOR HUMAN IDENTIFICATION BASED ON HUMAN RADIO BIOMETRIC INFORMATION
US - 27.02.2020
Int.Class G01S 7/41
GPHYSICS
01MEASURING; TESTING
SRADIO DIRECTION-FINDING; RADIO NAVIGATION; DETERMINING DISTANCE OR VELOCITY BY USE OF RADIO WAVES; LOCATING OR PRESENCE-DETECTING BY USE OF THE REFLECTION OR RERADIATION OF RADIO WAVES; ANALOGOUS ARRANGEMENTS USING OTHER WAVES
7Details of systems according to groups G01S13/, G01S15/, G01S17/127
02of systems according to group G01S13/58
41using analysis of echo signal for target characterisation; Target signature; Target cross-section
 Appl.No 16667757 Applicant ORIGIN WIRELESS, INC. Inventor Sai Deepika Regani

Methods, apparatus and systems for monitoring an object expression are described. In one example, a described apparatus in a venue comprises a receiver and a processor. The receiver is configured for: receiving a wireless signal from a transmitter through a wireless multipath channel that is impacted by an expression of an object in the venue, wherein the object has at least one movable part and is expressed in the expression with respect to a setup in the venue; and obtaining a time series of channel information (TSCI) of the wireless multipath channel based on the wireless signal received by the receiver. The processor is configured for computing information associated with the object based at least partially on the TSCI obtained when the object is expressed in the expression, and performing, based on the information associated with the object, a task associated with at least one of the object and the venue.

10.20220020155Image segmentation method using neural network based on mumford-shah function and apparatus therefor
US - 20.01.2022
Int.Class G06K 9/00
GPHYSICS
06COMPUTING; CALCULATING OR COUNTING
KGRAPHICAL DATA READING; PRESENTATION OF DATA; RECORD CARRIERS; HANDLING RECORD CARRIERS
9Methods or arrangements for recognising patterns
 Appl.No 17376588 Applicant Korea Advanced Institute of Science and Technology Inventor JongChul Ye

Disclosed is an image segmentation method including receiving an image to be segmented and segmenting the received image by using a neural network learned through a Mumford-Shah function-based loss function.

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